Image analysis of display content for dynamic adjustment of a continuous scan display

ABSTRACT

Various embodiments relating to reducing memory bandwidth consumed by a continuous scan display screen are provided. In one embodiment, scoring criteria are applied to a reference image of a first image format having a first bit depth to generate an image conversion score. The scoring criteria are based on a histogram of one or more characteristics of the reference image. If the image conversion score is greater than a threshold value, then the reference image is converted to a modified image of a second image format having a second bit depth less than the first bit depth, and the modified image is scanned onto the continuous scan display screen. If the image conversion score is less than the threshold value, then the reference image is scanned onto the continuous scan display screen.

BACKGROUND

High quality, high resolution displays are now widely used in variousmobile computing devices. Such high resolution displays require fastprocessing to deliver high resolution imagery, smooth user interfaceinteractions, fast Web page rendering, and quality 3D gaming, amongother operations. However, such features may consume a significantamount of power. In order to prolong battery life of a mobile computingdevice, power consumption of various hardware components of the mobilecomputing device may be reduced whenever possible without sacrificingnoticeable quality of displayed imagery or user interactions.

BRIEF DESCRIPTION OF THE. DRAWINGS

FIG. 1 schematically shows a computing system according to an embodimentof the present disclosure.

FIG. 2 shows an example of a histogram of pixel intensity of a referenceimage that may be determined to be suitable for image format conversionbased on an image conversion score of the reference image.

FIG. 3 shows an example of a histogram of pixel intensity of a referenceimage that may be determined to be unsuitable for image formatconversion based on an image conversion score of the reference image.

FIG. 4 shows an example of a score map applied to a histogram that isused to analyze whether a reference image ought to be converted to amodified image.

FIG. 5 shows an example of a histogram of color distance representativeof edge definition of a reference image.

FIG. 6 shows a method for switching an image format of an imagedisplayed by a continuous scan display screen according to an embodimentof the present disclosure.

FIG. 7 shows a method another method for switching an image format of animage displayed by a continuous scan display screen according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

The present description relates to managing system properties (forexample reducing memory bandwidth) consumed by a continuous scan displayscreen of a computing device. More particularly, the present descriptionrelates to various approaches for determining whether display content ofan image is suitable for down-converting the image scanned on thedisplay screen (e.g., reducing a bit depth of the image). For example,scoring criteria may be applied to a reference image to determine howmuch the reference image would be negatively affected by the downconversion. The scoring criteria may be based on a histogram of one ormore characteristics of the reference image. In particular, one or morethreshold values may be applied to the histogram to determine whetherthe characteristics of the reference image are suitable for downconversion. By detecting and distinguishing between images that areamenable to the pixel depth reduction, down-conversion of an image canbe applied with limited impact on a perceived quality of the image tothe user in order to reduce memory bandwidth consumed by scanning theimage to the display screen.

In some embodiments, once a reference image is determined to be downconverted (referred to herein as a down-conversion condition), furtheranalysis of the reference image may be performed to select a mostsuitable image format that limits impact on a perceived quality of theimage to the user beyond the reduced bit depth of the down-convertedimage. For example, some image formats may blur edges in an imagerelative to other image formats. In one example, edge detection analysismay be performed on a reference image to determine whether the referenceimage has relatively more edges or relatively fewer edges. If thereference image has more edges, then the reference image may beconverted to the image format that blurs relatively less edges. On theother hand, if the reference image has fewer edges, then the referenceimage may be converted to the image format that blurs relatively moreedges.

In some embodiments, an image may be down-converted and scanned onto thedisplay screen for a period when there is no change to the image inorder to reduce power consumption for the operation of the display. Thisperiod may be referred to herein as a static image period or a staticimage condition. Upon detecting a static image period, a reference imagemay be converted to a different image format with a lesser bit depth ifthe reference meets the scoring criteria, and the modified image may bedisplayed during the static image period. Selective use of this approachallows for higher definition when an image is being actively alteredwhile lowering a burden on system resources when there is no change tothe image. Such an approach may be preferably implemented in a mobilecomputing device, such as a system-on-chip (SOC) application, to achieveenergy efficiency, better thermal characteristics and/or prolong batterylife of the mobile computing device.

FIG. 1 schematically shows a computing system 100 according to anembodiment of the present disclosure. The computing system 100 may takethe form of one or more personal computers, home-entertainmentcomputers, network computing devices, gaming devices, mobile computingdevices (e.g., tablet), mobile communication devices (e.g., smartphone), and/or other computing devices. The computing system 100includes a processor 102 in communication with a storage device 104, adisplay pipeline 106, and a continuous scan display screen 108.

The processor 102 includes one or more processor cores, and instructionsexecuted thereon may be configured for sequential, parallel, and/ordistributed processing. The processor 102 includes one or more physicaldevices configured to execute instructions. For example, the processormay be configured to execute instructions that are part of one or moreapplications, programs, routines, libraries, objects, components, datastructures, or other logical constructs. Such instructions may beimplemented to perform a task, implement a data type, transform thestate of one or more components, achieve a technical effect, orotherwise arrive at a desired result.

In one example, the processor includes a central processing unit (CPU)and a graphics processing unit (GPU) that includes a plurality of cores.In this example, computation-intensive portions of instructions areexecuted in parallel by the plurality of cores of the GPU, while theremainder of the instructions is executed by the CPU. It will beunderstood that the processor may take any suitable form withoutdeparting from the scope of the present description.

The storage device 104 includes one or more physical devices configuredto hold instructions executable by the processor. When such instructionsare implemented, the state of the storage device may betransformed—e.g., to hold different data. The storage device may includeremovable and/or built-in devices. The storage device may includeoptical memory, semiconductor memory, and/or magnetic memory, amongothers. The storage device may include volatile, nonvolatile, dynamic,static, read/write, read-only, random-access, sequential-access,location-addressable, file-addressable, and/or content-addressabledevices. It will be understood that the storage device may take anysuitable form without departing from the scope of the presentdescription.

Storage locations of the storage device include a memory allocationaccessible by the processors during execution of instructions. Thismemory allocation can be used for execution of one or more softwarelayers 120 that may include an operating system 118 that managessoftware-level operation of the computing system.

The display pipeline 106 is configured to render a two-dimensionalraster representation of an image for display on the continuous scandisplay screen. The display pipeline includes a plurality of logic unitsand/or hardware units. In some embodiments, at least some of the unitsare fixed purpose or operation specific units. However, in someembodiments, some units are general purpose units that may performdifferent steps of the pipeline, and in some cases, perform generalpurpose computing.

One of the units of the display pipeline includes an image formatconverter 110 that may be implemented as hardware, firmware, orsoftware. The image format converter is configured to convert an imagefrom a first image format to a second image format that is differentfrom the first image format. In some cases, the image format converterdown-converts an image to a format having a lesser bit depth. In oneparticular example, the image format converter is configured to convertan image from a first image format that has a bit depth of 24 bits perpixel (e.g., 888 RGB) to a second image format that has a bit depth of16 bits per pixel (e.g., 565 RGB). In one example, the image formatconverter is configured to down-convert an image by truncating the bitdepth of each bit of the image format. By down-converting the image, amemory bandwidth utilized to scan the image onto the continuous scandisplay screen may be reduced, and correspondingly power consumption ofoperation of the continuous scan display screen may be reduced.

It will be understood that the visual appearance of the reference imageis not otherwise changed by the conversion beyond the down-conversion toa format having a lesser bit depth. It will be understood that the imageformat converter may convert an image from virtually any suitable imageformat to virtually any other suitable image format in any suitablemanner without departing from the scope of the present description. Forexample, in some embodiments, the image format converter is configuredto convert a reference image of a YUV image format to a modified imageof a different YUV format having a lesser bit depth. In another example,the image format converter is configured to convert a reference image ofa YUV image format to a modified image of an RGB image format. In yetanother example, the image format converter is configured to convert areference image of an RGB format to a modified image of a YUV imageformat.

In some embodiments, the image format converter is configured to convertthe reference image from a first image format to either a second imageformat or a third image format depending on characteristics of displaycontent of the reference image. In one example, the image formatconverter is configured to convert a reference image of an RGB format ofa higher bit depth to a modified image of an RGB format of a lower bitdepth or a YUV image format of a lower bit depth. In another example,the image format converter is configured to convert a reference image ofa YUV image format of a higher bit depth to a modified image of an RGBformat of a lower bit depth or a YUV image format of a lower bit depth.

In some embodiments, the processor, the storage device, and the displaypipeline may be implemented as a system-on-chip (SoC) 112. In a SoCimplementation, typically the processor, the storage device, and thedisplay pipeline, are formed as separate logic units within a single SoCintegrated circuit, and an on-chip communications interface enablescommunication between these separate logic units. Further, in someembodiments, the display pipeline may be physically integrated with theprocessor. In some embodiments, one or more of the processing steps maybe performed in software.

The continuous scan display screen 108 is used to present a visualrepresentation of data held by the storage machine in the form of animage. The image is rendered by the display pipeline. The continuousscan display screen is configured to repeatedly update a state of pixelsthat make up the display screen to display the image. In particular, thecontinuous scan display screen systematically processes the area of thepixels line by line or “scans” the pixels to update the image. Theprocessor is configured to control the continuous scan display screen byscanning an image produced by the display pipeline on to the continuousscan display screen.

As the herein described methods and processes may change the data heldby the storage device, and thus transform the state of the storagedevice, the state of display screen may likewise be transformed tovisually represent changes in the underlying data. In the illustratedexample, a reference image 114 of a first image format having a firstbit depth is stored in the storage device, and further scanned on thecontinuous scan display screen. The image format converter may convertthe reference image to a modified image 116 of a second image formathaving a second bit depth that is less than the first bit depth inresponse to determination of an operating condition, such as a staticimage condition that includes a period where an image remains unchangedor is not updated. The modified image may be stored in the storagedevice, and further scanned on the continuous scan display screen inplace of the reference image, under some conditions. In someembodiments, the reference image and/or the modified image may be storedin a buffer of the display pipeline, and scanned directly to thecontinuous scan display screen.

It will be understood that the continuous scan display screen is usedmerely as an example, and any type of display screen technology may beemployed. Such display devices may be combined with the processor andthe storage device in a shared enclosure, or such display devices may beperipheral display devices.

As discussed above, the computing system is configured to reduce memorybandwidth consumed by the continuous scan display screen when possible.In one example, the processor is configured to determine a static imageperiod where no updates are happening to the reference image and theimage is otherwise unmodified. For example, the static image period maybe defined as a period in which an image displayed on the continuousscan display screen is not expected to change for several frames. In oneexample, an indication of the static image period is received from theoperating system. For example, the indication of the static image periodmay include a command to stop generating or triggering imagesynchronization operation interrupts (e.g., VSYNC interrupts). Further,in response to determining the static image period, the processor isconfigured to trigger the image format converter to convert thereference image to the modified image, and scan the modified image ontothe continuous scan display screen during the static image period. Insome embodiments, the processor may be configured to delay triggeringthe image format converter for a designated duration from a start of theidle display condition. For example, the designated duration may lastfrom 1-2 seconds from the start of the idle display condition. Bydelaying conversion of the reference image for the designated duration,a likelihood of the display screen becoming active and ending the staticimage period may be reduced and confidence in the static image periodmay be increased. It will be understood that this technique and othertechniques may be used to improve instances when the conversion may beused.

Correspondingly, the processor is configured to determine an activeimage period or condition that occurs when the static image period isnot occurring. In one example, an indication of the active image periodis determined based on whether VSYNC interrupts are being generated bythe display pipeline. Further, the processor may be configured to scanthe reference image on to the continuous scan display screen during theactive image period.

In some embodiments, measures may be taken to determine whether areference image is a suitable candidate for conversion. In particular,an image quality of some images, as perceived by a user, may be morenegatively affected by a down-conversion than other images based onvarious characteristics of the images. For example, highly saturatedimages may be less affected by a reduction in bit depth than imageshaving a lower saturation level.

In one example, the processor is configured to apply scoring criteria tothe reference image to generate an image conversion score in response toan operating condition. For example, the operating condition may includea static image period of the continuous scan display screen. It will beunderstood that any suitable operating condition may be determined totrigger the analysis and down-conversion of an image without departingfrom the scope of the present description. The processor is furtherconfigured to compare the image conversion score to one or morethreshold values to yield either an affirmative output or a negativeoutput. If the comparison yields an affirmative output, the processorconverts the reference image to the modified image and scans themodified image onto the continuous scan display screen during theoperating condition. If the comparison yields a negative output, theprocessor scans the reference image onto the continuous scan displayscreen during the static image period.

In some embodiments, the scoring criteria are based on a histogram ofone or more characteristics of the reference image. In particular, thedisplay pipeline may be configured to generate various histograms ofdifferent image characteristics of the reference image during processingof the reference image. FIGS. 2-5 show examples of histograms that maybe generated by the display pipeline. The different histograms may beused to analyze whether the reference image is suitable fordown-conversion. For example, the histograms may be used by theprocessor to determine an image conversion score of the reference image.Further, the histograms may be used to select a most appropriate imageformat for down-conversion based on the characteristics of the referenceimage. The histograms include a plurality of different buckets thatrepresent different characteristic values of the reference image. Theheights of the buckets represent the frequency of the associatedcharacteristic values in the reference image. Non-limiting examples ofcharacteristics of the reference image that may be analyzed or otherwiseused to create a histogram include saturation level, pixel intensity,color distance, a number of edges, and other suitable imagecharacteristics.

FIGS. 2 and 3 show examples of histograms of pixel intensity of areference image that may be used to determine an image conversion score.Pixel intensity or a saturation level of the reference image may bedetermined in various manners. In some cases, the reference image is ofan RGB format that includes, for each pixel of the reference image, an Rchannel pixel intensity value, a G channel pixel intensity value, and aB channel pixel intensity value. In one example, the histogram ispopulated with a greatest pixel intensity value selected from the Rchannel pixel intensity value, the G channel pixel intensity value, andthe B channel pixel intensity value of each pixel. In other words, thehistogram may include buckets for each greatest intensity value ofpixels of the reference image.

In another example, the histogram is populated with the sum of the Rchannel pixel intensity value, the G channel pixel intensity value, andthe B channel pixel intensity value for each pixel. For example, thehistogram may be traversed on a per channel basis (e.g., three times,one for each of the three channels). In other words, the histogram mayinclude buckets for each pixel intensity value of pixels of thereference image. Alternatively, three separate channel-specifichistograms may be generated and analyzed to determine the imageconversion score of the reference image.

In still another example, the histogram is populated with a compositevalue of pixel intensity of each pixel. The composite value iscalculated by vector multiplying the three channels of the RGB formatwith different coefficients associated with each channel. In oneexample, a coefficient value 70 is associated with the R channel pixelintensity value, a coefficient value 20 is associated with the G channelpixel intensity value, and a coefficient value 10 is associated with theB channel pixel intensity.

FIG. 2 shows an example of a histogram 200 of pixel intensity of areference image. An image conversion score is generated based on thehistogram, and that image score is used to determine whether to downconvert to another image format (e.g., one with lower bit depth). Thehistogram includes a lower pixel intensity threshold value 202 and anupper pixel intensity threshold value 204 that are set to determine thesuitability of the reference image for image format conversion. It willbe understood that the upper and lower threshold values may be set toany suitable pixel intensity without departing from the scope of thepresent description. The histogram 200 indicates that a majority of thepixels of the image have intensities in the top-most buckets above theupper threshold value (close to fully saturated) or in the lowestbuckets below the lower threshold value (near black). The upper andlower threshold values are set as part of the scoring criteria, becausethe sorts of pixels above the upper threshold and below the lowerthreshold may be nearly indistinguishable between an image format havinggreater bit depth (e.g., 32 bpp) and an image format having lesser bitdepth (e.g., 16 bpp). In other words, an image having a relatively highpercentage of pixel intensities in these buckets is a suitable candidatefor down-conversion, in the sense that down-conversion will have minimalor no effect upon the viewer's perception of the image.

FIG. 3 shows an example of another histogram 300 of pixel intensity of areference image. In this case, histogram 300 indicates that a relativelyhigh percentage of the pixels have intensities in the middle bucketsbelow the upper threshold value and above the lower threshold value.These sorts of pixels are more negatively affected by a down-conversionof the reference image as perceived by the user. As such, it may bedetermined that a reference image with these characteristics is not asuitable candidate for down-conversion into a modified image format.

A dot product calculation may be used in connection with the histogramsin order to generate the image conversion score. Specifically, the imageconversion score may be arrived at by taking the dot product of acoefficient vector and a frequency vector. The coefficient vectorincludes a coefficient for each bucket of the histogram. An examplecoefficient vector is shown at 402 in FIG. 4. Specifically, the examplecoefficient vector is {4,3,2,1,−1 . . . 4}. The frequency vector is thevalue in each bucket (e.g., the number of pixels having a givenintensity). The image conversion score is generated by taking the dotproduct of these two vectors. In this example, a higher image conversionscore means that the image is relatively more desirable as a candidatefor down-conversion. Conversely, a relatively lower score means that theimage is not a good candidate for down-conversion. A threshold may beestablished in this regard, such that down-conversion is only performedif the image conversion score exceeds a threshold.

From the above, it will be appreciated that the coefficient vectortypically is constructed to weight certain characteristic values (e.g.,intensity values) relatively higher than other values. The examplevector of FIG. 4 has the effect of generating a higher image conversionscore when the image has a relatively high number of pixels with highand/or low intensities (e.g., below lower threshold 404 and/or abovethreshold 406). A high percentage of pixels in the middle intensities(between thresholds 404 and 406) yields a lower image conversionscore—i.e., that the image is not a good candidate for down-conversion.

Different image formats may have different effects on a down-convertedimage. For example, some image formats may be more apt to blur edges inan image. As one example, a planar YUV format may cause such blurring.Accordingly, in some embodiments, an amount of edge definition of thereference image (i.e., a number of edges in the reference image) is usedas scoring criteria to calculate an image conversion score or otherwisedetermine whether it is appropriate to down-convert.

In one example, the scoring criteria applied to the reference image todetermine whether the reference image is suitable for down-conversionare based on a number of edges in the reference image. For example, athreshold value of edges may be set to indicate that the reference imageincludes enough edges to make it unsuitable for conversion to the YUVimage format. If the reference image has a number of edges greater thanthe threshold value, then the reference image is scanned onto thecontinuous scan display screen. On the other hand, if the referenceimage has a number of edges less than the threshold value, then thereference image is converted to a converted image having the YUV formatand the converted image is scanned onto the continuous scan displayscreen.

In another example, the number of edges is used as scoring criteria in adetermination of selecting an appropriate image format for downconversion. For example, a determination may be made to down convert areference image based on any suitable scoring criteria corresponding toany suitable image characteristic values of the reference image. Oncethe decision to down convert the reference image is made, a seconddetermination is made using the number of edges in the reference imageas the scoring criteria. In particular, if the number of edges of thereference image is greater than the threshold value, then the referenceimage is converted to a converted image of the RGB format and theconverted image is scanned onto the continuous scan display screen. Onthe other hand, if the number of edges of the reference image is lessthan the threshold value, then the reference image is converted to aconverted image of the YUV format and the converted image is scannedonto the continuous scan display screen.

Note that, in this example, both of the RGB and YUV formats have bitdepths that are less than a bit depth of the original format of thereference image. The RGB format may be more appropriate for an imagethat has more edges, because the RGB format blurs edges less than theYUV format. On the other hand, in some cases, the YUV format may providericher color definition than the RGB format, and thus may be a suitableselection for down conversion. Further, it will be appreciated that thereference image may be down-converted from any suitable format to theRGB format, the YUV format, or another image format.

In one example, the reference image is of a YUV format. The YUV formatincludes a luma value and chroma coordinate values in the image. Theluma value defines a brightness of the reference image on a per pixelbasis. The chroma coordinates values define colors of four pixel blocksof the reference image. The chroma coordinate values may be used tocalculate a color distance between blocks on a color gamut. Moreover,edge definition of the reference image may be characterized by ahistogram of color distance. FIG. 5 shows an example of a histogram 500of color distance representative of edges in a reference image. Thehistogram includes buckets for each distance value between the chromacoordinate values in the reference image. A threshold distance value 502may be set to determine at which color distance an edge is defined. Inother words, if a color distance is greater than the threshold distancevalue, then that color distance is considered to be an edge in thereference image. On the other hand, if a color distance is less than thethreshold distance value, then that color distance is considered to notbe an edge in the reference image.

In one example, the image conversion score for the histogram 500 isdetermined by comparing an accumulation of distance values greater thanthe threshold distance value with an accumulation of distance valuesless than the threshold distance value. If the accumulation of distancevalues greater than the threshold distance value is greater than theaccumulation of distance values less than the threshold distance value,then an affirmative output is yielded. On the other hand, if theaccumulation of distance values greater than the threshold distancevalue is less than the accumulation of distance values less than thethreshold distance value, then a negative output is yielded. In oneexample, the reference image is converted to the YUV format and scannedonto the continuous scan display screen if the positive output isyielded. Further, the reference image is converted to the RGB format andscanned onto the continuous scan display screen if the negative outputis yielded.

It will be understood that edge definition of an image may be determinedin any suitable manner without departing from the scope of the presentdisclosure. It will be understood that any suitable characteristics maybe included in the scoring criteria without departing from the scope ofthe present description. It will be appreciated that the hereindescribed YUV format may include any suitable variation or derivativeincluding Y′UV, Y″UV, YCbCr, YPbPr, etc.

FIG. 6 shows a method 600 for switching an image format of an imagedisplayed by a continuous scan display screen according to an embodimentof the present disclosure. For example, the method 600 may be performedby the processor 102 of the computing system shown in FIG. 1.

At 602, the method 600 includes determining whether there is a staticimage period of the display screen. In one example, the determinationmay be made in response to receiving, from an operating system, anindication that the operating system does not expect to change an imagefor a plurality of image frames. Such an indication may include acommand to stop generating image synchronization operation interrupts.It will be understood that the determination may be made in any suitablemanner. If it is determined that there is a static image period, thenthe method 600 moves to 604. Otherwise, the method 600 moves to 616.

At 604, the method 600 includes determining whether a designatedduration since a start of the static image period has elapsed. Forexample, the designated duration may be from 1-2 seconds. If it isdetermined that the designated duration has elapsed, then the method 600moves to 606. Otherwise, the method 600 returns to 604.

At 606, the method 600 includes applying scoring criteria to thereference image to generate an image conversion score for the referenceimage. For example, the scoring criteria may be based at least partiallyon a saturation level of the reference image, edge definition, oranother characteristic of the reference image. In some embodiments, thescoring criteria are based on a histogram of one or more characteristicsof the reference image. In one example, the histogram is generated bythe display pipeline hardware. As another example, the histogram isgenerated by logic in the GPU.

At 608, the method 600 includes comparing the image conversion score toone or more threshold values. Such comparisons yield either anaffirmative output or a negative output. The affirmative outputindicates that the reference image is a relatively good candidate fordown-conversion. The negative output indicates that the reference imageis a relatively poor candidate for down-conversion.

At 610, the method 600 includes determining whether the comparisonyields an affirmative output or a negative output. If it is determinedthat the comparison yields the affirmative output, then the method 600moves to 612. Otherwise, the method 600 moves to 616.

At 612, the method 600 includes converting the reference image of afirst image format having a first bit depth to a modified image of asecond image format having a second bit depth that is less than thefirst bit depth. In one example, the first image format has a bit depthof 24 bits per pixel and the second image format has a bit depth of 16bits per pixel.

At 614, the method 600 includes scanning the modified image onto thecontinuous scan display screen. The modified image is scanned onto thedisplay screen during the static image period in order to reduce memorybandwidth consumed by the display screen. In one example, the modifiedimage is scanned onto the display screen for the entirety of the staticimage period.

On the other hand, if it is determined that there is an active imageperiod that occurs when the static image period is not occurring, orthat the image conversion score of the reference image yields a negativeoutput when compared to the one or more threshold values, at 616, themethod 600 includes scanning the reference image onto the continuousscan display screen. The reference image is scanned onto the displayscreen, because it is determined that the operating conditions and/orthe characteristics of the reference image would cause display of acorresponding modified image to be negatively perceived by the user.

FIG. 7 shows a method 700 for switching an image format of an imagedisplayed by a continuous scan display screen according to an embodimentof the present disclosure. For example, the method 700 may be performedby the processor 102 of the computing system shown in FIG. 1.

At 702, the method 700 includes determining an image down-conversioncondition where a reference image is converted from a first image formatwith a first bit depth to a modified image of a different image formathaving a bit depth less than the first bit depth. In one example, thedown-conversion condition is determined by performing steps 602-612 ofmethod 600. In one example, the image down-conversion condition occursduring a static image period of the continuous scan display screen. Ifit is determined that there is an image down-conversion condition, thenthe method 700 moves to 704. Otherwise, the method 700 returns to otheroperations.

At 704, the method 700 includes applying scoring criteria to thereference image to generate an image conversion score. In one example,the scoring criteria are based on a histogram of one or morecharacteristics of the reference image. Non-limiting examples ofcharacteristics on which the scoring criteria are based or otherwisederived include saturation level, pixel intensity, edge definition(i.e., a number of edges), color distance, etc.

At 706, the method 700 includes comparing the image conversion score toone or more threshold values. Such comparisons yield either anaffirmative output or a negative output.

At 708, the method 700 includes determining whether the comparisonyields an affirmative output or a negative output. If it is determinedthat the comparison yields an affirmative output, then the method 700moves to 710. Otherwise, the method 700 moves to 714.

At 710, the method 700 includes converting the reference image to amodified image of a second image format having a second bit depth lessthan the first bit depth. In one example, the reference image isdetermined to have a number of edges greater than a threshold number ofedges, so the reference image is converted to the RGB format. In otherwords, the scoring criteria indicate that the relatively high edgedefinition of the reference image make the reference image a goodcandidate for conversion to the RGB format.

At 712, the method 700 includes scanning the modified image onto thecontinuous scan display screen during the down-conversion condition.

At 714, the method 700 includes converting the reference image to amodified image of a third image format having a third bit depth lessthan the first bit depth. In one example, the reference image isdetermined to have a number of edges less than a threshold number ofedges, so the reference image is converted to the YUV format. In otherwords, the scoring criteria indicate that the relatively low edgedefinition of the reference image make the reference image a goodcandidate for conversion to the YUV format.

At 716, the method 700 includes scanning the modified image onto thecontinuous scan display screen during the down-conversion condition.

It will be understood that methods described herein are provided forillustrative purposes only and are not intended to be limiting.Accordingly, it will be appreciated that in some embodiments the methodsdescribed herein may include additional or alternative steps orprocesses, while in some embodiments, the methods described herein mayinclude some steps or processes that may be reordered, performed inparallel or omitted without departing from the scope of the presentdisclosure. In some embodiments, portions of the method may be combinedor otherwise performed in conjunction.

It will be understood that while the examples above focus on continuousscan displays of mobile computing devices, the description may bebroadly applicable to other electronic devices. Moreover, it will beunderstood that the concepts discussed herein may be broadly applicableto dynamically altering display content and system properties associatedwith the display content in order to optimize energy consumption forscan out to a continuous scan display screen. Furthermore, it will beunderstood that the methods described herein may be performed using anysuitable software and hardware in addition to or instead of the specificexamples described herein. The subject matter of the present disclosureincludes all novel and non-obvious combinations and sub-combinations ofthe various processes, systems and configurations, and other features,functions, acts, and/or properties disclosed herein, as well as any andall equivalents thereof. It will be understood that the configurationsand/or approaches described herein are exemplary in nature, and thatthese specific embodiments or examples are not to be considered in alimiting sense, because numerous variations are possible.

1. A method for switching an image format of an image displayed by acontinuous scan display screen of a computing device, the methodcomprising: applying scoring criteria to a reference image of a firstimage format having a first bit depth to generate an image conversionscore, where the scoring criteria is based on a histogram of one or morecharacteristics of the reference image; comparing the image conversionscore to one or more threshold values, where such comparing yieldseither an affirmative output or a negative output; if the comparing stepyields the affirmative output, (i) converting the reference image to amodified image of a second image format having a second bit depth lessthan the first bit depth, and (ii) scanning the modified image onto thecontinuous scan display screen; and if the comparing step yields thenegative output, scanning the reference image onto the continuous scandisplay screen.
 2. The method of claim 1, where the scoring criteria isapplied in response to a static image period of the continuous scandisplay screen.
 3. The method of claim 1, where the first image formatincludes an RGB format including an R channel pixel intensity value, a Gchannel pixel intensity value, and a B channel pixel intensity value,where the one or more characteristics of the reference image include agreatest pixel intensity value selected from the R channel pixelintensity value, the G channel pixel intensity value, and the B channelpixel intensity value, and where the histogram includes buckets for eachgreatest intensity value of pixels of the reference image.
 4. The methodof claim 1, where the first image format includes an RGB formatincluding an R channel pixel intensity value, a G channel pixelintensity value, and a B channel pixel intensity value, where the one ormore characteristics of the reference image include the R channel pixelintensity value, the G channel pixel intensity value, and the B channelpixel intensity value, and where the histogram includes buckets for eachpixel intensity value of pixels of the reference image.
 5. The method ofclaim 1, where the first image format includes a YUV format including aluma value and chroma coordinate values, where the one or morecharacteristics of the reference image include a distance value betweenthe chroma coordinate values on a color gamut, and where the histogramincludes buckets for each distance value between the chroma coordinatevalues in the reference image.
 6. The method of claim 5, where the oneor more threshold values includes a threshold distance value, where ifan accumulation of distance values greater than the threshold distancevalue is greater than an accumulation of distance values less than thethreshold distance value, then the affirmative output is yielded, andwhere if the accumulation of distance values greater than the thresholddistance value is less than the accumulation of distance values lessthan the threshold distance value, then the negative output is yielded.7. The method of claim 1, where generating the image conversion scoreincludes taking a dot product of a coefficient vector and a frequencyvector, where the coefficient vector includes a plurality ofcoefficients, each coefficient associated with a different bucket of thehistogram, and where the frequency vector includes a plurality offrequencies, each frequency associated with a different bucket of thehistogram.
 8. The method of claim 1, where the one or morecharacteristics of the reference image includes a number of edges in thereference image, where the one or more threshold values includes athreshold number of edges, and where the reference image is converted tothe converted image having a YUV format if the number of edges of thereference image is less than the threshold number of edges.
 9. Acomputing system, comprising: a continuous scan display screen; aprocessor; and a storage device holding instructions that when executedby the processor: apply scoring criteria to a reference image of a firstimage format having a first bit depth to generate an image conversionscore, where the scoring criteria is based on a histogram of one or morecharacteristics of the reference image; compare the image conversionscore to one or more threshold values, where such comparing yieldseither an affirmative output or a negative output; if the comparing stepyields the affirmative output, (i) convert the reference image to amodified image of a second image format having a second bit depth lessthan the first bit depth, and (ii) scan the modified image onto thecontinuous scan display screen; and if the comparing step yields thenegative output, scan the reference image onto the continuous scandisplay screen.
 10. The computing system of claim 9, where the scoringcriteria is applied in response to a static image period of thecontinuous scan display screen.
 11. The computing system of claim 9,where the first image format includes an RGB format including an Rchannel pixel intensity value, a G channel pixel intensity value, and aB channel pixel intensity value, where the one or more characteristicsof the reference image include a greatest pixel intensity value selectedfrom the R channel pixel intensity value, the G channel pixel intensityvalue, and the B channel pixel intensity value, and where the histogramincludes buckets for each greatest intensity value of pixels of thereference image.
 12. The computing system of claim 9, where the firstimage format includes an RGB format including an R channel pixelintensity value, a G channel pixel intensity value, and a B channelpixel intensity value, where the one or more characteristics of thereference image include the R channel pixel intensity value, the Gchannel pixel intensity value, and the B channel pixel intensity value,and where the histogram includes buckets for each pixel intensity valueof pixels of the reference image.
 13. The computing system of claim 9,where the first image format includes a YUV format including a lumavalue and chroma coordinate values, where the one or morecharacteristics of the reference image include a distance value betweenthe chroma coordinate values on a color gamut, and where the histogramincludes buckets for each distance value between the chroma coordinatevalues in the reference image.
 14. The computing system of claim 9,where generating the image conversion score includes taking a dotproduct of a coefficient vector and a frequency vector, where thecoefficient vector includes a plurality of coefficients, eachcoefficient associated with a different bucket of the histogram, andwhere the frequency vector includes a plurality of frequencies, eachfrequency associated with a different bucket of the histogram.
 15. Thecomputing system of claim 9, where the one or more characteristics ofthe reference image includes a number of edges in the reference image,where the one or more threshold values includes a threshold number ofedges, and where the reference image is converted to the modified imagehaving a YUV format if the number of edges of the reference image isless than the threshold number of edges.
 16. A method for switching animage format of an image displayed by a continuous scan display screenof a computing device, the method comprising: determining an imagedown-conversion condition where a reference image is converted from afirst image format with a first bit depth to a modified image of adifferent image format having a bit depth less than the first bit depth;applying scoring criteria to the reference image to generate an imageconversion score; comparing the image conversion score to one or morethreshold values, where such comparing yields either an affirmativeoutput or a negative output; if the comparing step yields theaffirmative output, (i) converting the reference image to a modifiedimage of a second image format having a second bit depth less than thefirst bit depth, and (ii) scanning the modified image onto thecontinuous scan display screen during the down-conversion condition; andif the comparing step yields a negative output, (i) converting thereference image to a modified image of a third image format having athird bit depth less than the first bit depth, and (ii) scanning themodified image onto the continuous scan display screen during thedown-conversion condition.
 17. The method of claim 16, where the scoringcriteria is based on a histogram of one or more characteristics ofpixels of the reference image.
 18. The method of claim 16, where theimage down-conversion condition occurs during a static image period ofthe continuous scan display screen.
 19. The method of claim 16, wherethe second image format is an RGB format and the third image format is aYUV format.
 20. The method of claim 19, where applying the scoringcriteria includes determining a number of edges in the reference image,where if the number of edges of the reference is greater than athreshold number of edges, then the reference image is converted to theRGB format, and where if the number of edges of the reference image isless than the threshold number of edges, then the reference image isconverted to the YUV format.